To reduce annotation effort, most existing approaches rely on semi-supervised training schemes, which use a small set of strong annotations and a large set of weak annotations, such as scribbles [1]} [2]}, bounding boxes [3]} or image-level class labels [4]} [5]} [6]}. Among three types of weak annotations, image-level class labels are the easiest one to obtain. Class Activation Map (CAM) [7]} is a good choice for generating pixel-level labels from image-level annotations, which can roughly localize object areas by drawing attentions on discriminative regions of class-specific objects. However, such localization maps are coarse and only activate parts of target objects. To address this issue, several techniques have been proposed to expand these activated regions to the whole target object [8]} [9]}[10]} [5]} [12]}. However, most of those methods need to carefully choose seed regions, which are only based on confidence maps produced by CAM. So they do not make full use of pixel-level strong supervisions when generating pseudo labels from weak annotations. Recently, a few studies provide novel perspectives to solve this problem. [13]} designs a strong-weak dual-branch network to exploit strong and weak annotations. [14]} proposes a consistency training to enforce an invariance of the model's predictions over small perturbations applied to the inputs.
